A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease

Nicholas J. Ashton, Alejo J. Nevado-Holgado, Imelda S. Barber, Steven Lynham, Veer Gupta, Pratishtha Chatterjee, Kathryn Goozee, Eugene Hone, Steve Pedrini, Kaj Blennow, Michael Schöll, Henrik Zetterberg, Kathryn A. Ellis, Ashley I. Bush, Christopher C. Rowe, Victor L. Villemagne, David Ames, Colin L. Masters, Dag Aarsland, John Powell & 3 others Simon Lovestone, Ralph Martins, Abdul Hye

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

Original languageEnglish
Article numbereaau7220
JournalScience Advances
Volume5
Issue number2
DOIs
Publication statusPublished - 6 Feb 2019

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classifiers
Amyloid
Proteomics
Blood Proteins
Alzheimer Disease
proteins
Positron-Emission Tomography
Mass Spectrometry
machine learning
Proteins
pathology
Pathology
Sensitivity and Specificity
fractionation
Peptides
learning
blood
peptides
positrons
mass spectroscopy

Cite this

Ashton, N. J., Nevado-Holgado, A. J., Barber, I. S., Lynham, S., Gupta, V., Chatterjee, P., ... Hye, A. (2019). A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Science Advances, 5(2), [eaau7220]. https://doi.org/10.1126/sciadv.aau7220
Ashton, Nicholas J. ; Nevado-Holgado, Alejo J. ; Barber, Imelda S. ; Lynham, Steven ; Gupta, Veer ; Chatterjee, Pratishtha ; Goozee, Kathryn ; Hone, Eugene ; Pedrini, Steve ; Blennow, Kaj ; Schöll, Michael ; Zetterberg, Henrik ; Ellis, Kathryn A. ; Bush, Ashley I. ; Rowe, Christopher C. ; Villemagne, Victor L. ; Ames, David ; Masters, Colin L. ; Aarsland, Dag ; Powell, John ; Lovestone, Simon ; Martins, Ralph ; Hye, Abdul. / A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. In: Science Advances. 2019 ; Vol. 5, No. 2.
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title = "A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease",
abstract = "A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.",
author = "Ashton, {Nicholas J.} and Nevado-Holgado, {Alejo J.} and Barber, {Imelda S.} and Steven Lynham and Veer Gupta and Pratishtha Chatterjee and Kathryn Goozee and Eugene Hone and Steve Pedrini and Kaj Blennow and Michael Sch{\"o}ll and Henrik Zetterberg and Ellis, {Kathryn A.} and Bush, {Ashley I.} and Rowe, {Christopher C.} and Villemagne, {Victor L.} and David Ames and Masters, {Colin L.} and Dag Aarsland and John Powell and Simon Lovestone and Ralph Martins and Abdul Hye",
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Ashton, NJ, Nevado-Holgado, AJ, Barber, IS, Lynham, S, Gupta, V, Chatterjee, P, Goozee, K, Hone, E, Pedrini, S, Blennow, K, Schöll, M, Zetterberg, H, Ellis, KA, Bush, AI, Rowe, CC, Villemagne, VL, Ames, D, Masters, CL, Aarsland, D, Powell, J, Lovestone, S, Martins, R & Hye, A 2019, 'A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease' Science Advances, vol. 5, no. 2, eaau7220. https://doi.org/10.1126/sciadv.aau7220

A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. / Ashton, Nicholas J.; Nevado-Holgado, Alejo J.; Barber, Imelda S.; Lynham, Steven; Gupta, Veer; Chatterjee, Pratishtha; Goozee, Kathryn; Hone, Eugene; Pedrini, Steve; Blennow, Kaj; Schöll, Michael; Zetterberg, Henrik; Ellis, Kathryn A.; Bush, Ashley I.; Rowe, Christopher C.; Villemagne, Victor L.; Ames, David; Masters, Colin L.; Aarsland, Dag; Powell, John; Lovestone, Simon; Martins, Ralph; Hye, Abdul.

In: Science Advances, Vol. 5, No. 2, eaau7220, 06.02.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease

AU - Ashton, Nicholas J.

AU - Nevado-Holgado, Alejo J.

AU - Barber, Imelda S.

AU - Lynham, Steven

AU - Gupta, Veer

AU - Chatterjee, Pratishtha

AU - Goozee, Kathryn

AU - Hone, Eugene

AU - Pedrini, Steve

AU - Blennow, Kaj

AU - Schöll, Michael

AU - Zetterberg, Henrik

AU - Ellis, Kathryn A.

AU - Bush, Ashley I.

AU - Rowe, Christopher C.

AU - Villemagne, Victor L.

AU - Ames, David

AU - Masters, Colin L.

AU - Aarsland, Dag

AU - Powell, John

AU - Lovestone, Simon

AU - Martins, Ralph

AU - Hye, Abdul

PY - 2019/2/6

Y1 - 2019/2/6

N2 - A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

AB - A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

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U2 - 10.1126/sciadv.aau7220

DO - 10.1126/sciadv.aau7220

M3 - Article

VL - 5

JO - Science Advances

JF - Science Advances

SN - 2375-2548

IS - 2

M1 - eaau7220

ER -

Ashton NJ, Nevado-Holgado AJ, Barber IS, Lynham S, Gupta V, Chatterjee P et al. A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Science Advances. 2019 Feb 6;5(2). eaau7220. https://doi.org/10.1126/sciadv.aau7220